Data Scientist – Materials R&D

IPG

. Partner with polymer scientists, chemists, and engineers to support bio-polymer research and development using data-driven methods .

Posted 10/9/2026full-timeRemote • Michigan • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in data analysis and modeling using Python and R, with a strong foundation in statistics and experimental design. Proven ability to apply machine learning techniques to complex scientific datasets and collaborate effectively in cross-functional R&D environments.

Highest-signal resume keywords

Data Analysis Using PythonMachine Learning TechniquesStatistical AnalysisExperimental DesignMentoring Technical Staff

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceApplied AnalyticsStatistical AnalysisMultivariate AnalysisPredictive ModelingData Workflow DevelopmentExperimental DesignData CleaningModel DeploymentFormulation Design

Soft Skills

Strong Communication SkillsCollaborationMentoring

Tools & Technologies

SQLDOE SoftwareLaboratory Data Management Systems (LIMS)AWSAzure

Industry Keywords

Bio-PolymersMaterials SciencePolymer ScienceChemical R&DData Science Lifecycle

Tech Stack

Tools & technologies

AWSAzureCloudPythonSQL

About the role

Key responsibilities & impact

Partner with polymer scientists, chemists, and engineers to support bio-polymer research and development using data-driven methods
Analyze and model experimental, formulation, and process data to identify structure-property-process relationships
Develop predictive models for material performance and property optimization, formulation design and screening, and scale-up and process optimization
Design and analyze experiments (DOE) to maximize learning efficiency and reduce development timelines
Build and maintain reproducible data workflows for R&D data ingestion, cleaning, and analysis
Apply machine learning techniques including regression, classification, clustering, and time-series modeling to complex scientific datasets
Collaborate with data engineering and IT teams to enable scalable data infrastructure for R&D
Communicate insights, tradeoffs, and recommendations to technical and non-technical stakeholders
Contribute to data dictionaries and process flow diagrams for complex data solutions
Mentor junior data scientists or technical staff and contribute to data science best practices within R&D
Stay current with advances in materials informatics, polymer modeling, and applied AI in scientific research

Requirements

What you’ll need

Bachelor’s degree in Data Science, Computer Science, Statistics, Materials Science, Chemical Engineering, or a related field; Master’s or PhD preferred
10+ years of professional experience in data science, applied analytics, or scientific computing
Experience working with materials science, polymer science or chemical R&D data preferred
Strong proficiency in Python and/or R for data analysis and modeling
Solid experience with SQL and structured and semi-structured datasets
Strong foundation in statistics, experimental design, and multivariate analysis
Demonstrated experience applying machine learning to real-world, noisy scientific or experimental data
Ability to work effectively in a cross-functional R&D environment
Strong communication skills and ability to translate complex analyses into actionable insights
Familiarity with bio-polymers, sustainable materials, or polymer processing preferred
Experience with DOE software, laboratory data management systems (LIMS), or scientific databases preferred
Experience deploying models to support R&D decision-making or manufacturing scale-up preferred
Familiarity with cloud platforms such as AWS or Azure and data science lifecycle tools preferred
Prior experience mentoring or leading technical projects preferred